Research & Education

OpenNE is an open-source package designed for network embedding (NE) and serves as a comprehensive toolkit for network representation learning (NRL). It offers a standardized interface for both training and testing different NE models, ensuring scalability and flexibility. The package includes implementations of several typical NE models, such as DeepWalk, LINE, node2vec, GraRep, GCN, HOPE, GF, SDNE, and LE. A key feature is TADW, which allows for the incorporation of text attributes of nodes, enhancing the embedding process. OpenNE leverages TensorFlow, enabling GPU-accelerated training for improved performance. The toolkit also provides evaluation capabilities through node classification tasks, reporting Micro-F1, Macro-F1, and running time for various methods and datasets like Wiki and Cora.

RepDistiller is an open-source project that implements Contrastive Representation Distillation (CRD), as detailed in its ICLR 2020 paper. Beyond CRD, it serves as a comprehensive benchmark for 12 state-of-the-art knowledge distillation methods within the PyTorch framework. This tool enables users to run various distillation experiments, train student networks from pre-trained teacher models, and combine different distillation objectives. It provides scripts for fetching pre-trained teachers and running distillation processes, making it a valuable resource for researchers and developers working on model compression and efficiency.

GoLove.ai is an AI Girlfriend chatbot app designed to provide users with a virtual companion for realistic conversations. It allows users to chat, receive pictures and voice messages, and engage in emotional interactions with their AI girlfriend. The platform remembers past conversations, preferences, and relationship history to foster a deeper connection. While basic text chats, emotional conversations, relationship memory, voice messages, and safe-for-work pictures are free and unlimited after registration, NSFW content, including uncensored talks, spicy role-play, naked AI girlfriend images, and adult videos, requires a paid subscription. The service is web-based, requiring no downloads, and offers a gallery of ready-made AI girlfriends, with a custom creator feature coming soon.

Valence Labs, powered by Recursion, functions as an AI research engine dedicated to accelerating the mission of decoding biology to radically improve lives. The platform utilizes Recursion’s extensive data generation capabilities, including over 60 petabytes of phenomics and transcriptomics data, alongside its BioHive supercomputer for massive computing power. Valence Labs focuses on predicting cellular responses to perturbations, explaining molecular interactions, and discovering novel therapeutic hypotheses through lab-in-the-loop engines. This approach aims to build 'virtual cells'—mechanistic models of cellular function—to guide the discovery of new therapeutics and improve drug discovery outcomes.

AI4CCAM is a collaborative research project dedicated to advancing Trustworthy AI for Connected, Cooperative, and Automated Mobility (CCAM). The project brings together a consortium of 14 partners, including seven research organizations, four small and medium enterprises, one large company, and two user associations. Their primary goal is to design and implement robust AI methods and tools specifically tailored for autonomous vehicles. With a duration of 36 months and a substantial budget, AI4CCAM aims to address the critical need for reliable and safe AI solutions in the evolving landscape of automated transportation, contributing to the future deployment of autonomous vehicles.

Cortical Labs pioneers a new era of computing by merging lab-grown neurons with silicon chips, creating what they term 'actual intelligence.' Their flagship product, the CL1, is the world’s first code-deployable biological computer, allowing users to test how lab-grown neurons process information and learn. Complementing this, the Cortical Cloud provides a biological cloud computing platform, enabling users to build breakthrough technology without needing a specialized lab. This innovative approach leverages real neurons' intuitive learning capabilities and remarkable efficiency, requiring minimal energy and training data compared to traditional AI. The technology aims to revolutionize fields like healthcare discoveries, personalized medicine, and enhanced cell therapies by solving profound problems with biocomputing's energy efficiency.

Open Notebook is a powerful open-source, AI-powered note-taking and research platform designed for individuals who value privacy and control over their data. It allows users to leverage AI for summarizing notes, generating insights, and efficient note management. A standout feature is its Podcast Generator, which transforms notes into engaging podcasts with customizable voices and speakers. The platform supports various content integrations, including links, PDFs, TXT, PPT, and YouTube, making it versatile for different research needs. Open Notebook emphasizes privacy control, allowing users to decide what information AI can access and how their data is used, making it ideal for researchers, students, and professionals seeking enhanced learning while maintaining data sovereignty.

DINOv3 Video Tracking is an innovative in-browser video tracking tool, powered by Transformers.js, designed for analyzing video content directly within your web browser. Users can upload any video file or select an example, then process it to analyze each frame. The core functionality allows you to click on a specific region in the video to mark an object of interest. The application then intelligently highlights similar patches throughout the entire video, making it easy to track objects across different frames. This tool is particularly useful for demonstration and research purposes, offering a straightforward way to visualize object persistence and similarity within video sequences without requiring complex software installations.

Apollo AI is the #1 AI academic researcher designed for students and academics. It allows users to instantly analyze PDFs of any length, summarize YouTube videos, and generate publication-ready charts and visualizations. The tool conducts deep web research with verified academic sources, identifies research gaps, and synthesizes findings into comprehensive reports with proper citations. Apollo AI also features real-time voice AI for hands-free research, multi-language support (including English, Arabic, Chinese, and French), and a literature review assistant to synthesize multiple research papers. It offers a generous free tier with core AI research features, making it accessible for a wide range of users.

papers_for_protein_design_using_DL is a GitHub repository offering a meticulously curated list of research papers focused on the intersection of protein design and deep learning techniques. This resource is invaluable for academics, researchers, and students in computational biology, bioinformatics, and related fields who need to stay abreast of the latest advancements. The repository is inspired by the work of Kevin Kaichuang Yang and aims to centralize key literature, making it easier for the community to access and explore foundational and cutting-edge research in this rapidly evolving domain. It acts as a specialized bibliography, streamlining the literature review process for those working on protein design challenges using AI.

Awesome-AutoML-and-Lightweight-Models is a comprehensive GitHub repository that curates high-quality and recent works in the field of Automated Machine Learning (AutoML) and lightweight models. It serves as a valuable resource for researchers and practitioners, categorizing information into key areas such as Neural Architecture Search, Lightweight Structures, Model Compression, Quantization and Acceleration, Hyperparameter Optimization, and Automated Feature Engineering. The repository includes links to papers and associated code repositories (often in PyTorch or TensorFlow), making it easy to explore and implement the discussed techniques. It is continuously updated, welcoming contributions to ensure it remains a current and relevant resource for the AutoML research community.

Awesome-Spiking-Neural-Networks is a comprehensive, open-source repository dedicated to collecting and organizing research papers, associated code, and relevant websites pertaining to spiking neural networks (SNNs). The project is diligently maintained and continuously updated, ensuring that users have access to the most recent advancements in the field. It features papers from prestigious conferences like AAAI, ICLR, NeurIPS, ICML, and CVPR, as well as top-tier journals such as Nature, Science, and Cell. This resource is invaluable for researchers, academics, and students looking to stay current with the cutting edge of SNN development, providing a centralized hub for critical information and resources.

awesome-ml4co is a meticulously curated collection of resources focusing on the application of machine learning technologies to solve combinatorial optimization problems. Maintained by members of SJTU-Thinklab, this GitHub repository serves as a central hub for researchers and academics. It categorizes resources by problem type, including Job Shop Scheduling, Traveling Salesman Problem, Graph Matching, and many more, alongside a dedicated section for survey papers. The repository highlights contributions from Thinklab members and actively seeks post-docs interested in areas like learning combinatorial solvers, dynamic graphs, and reinforcement learning, fostering community contribution to this evolving field.

Data-Analysis-and-Machine-Learning-Projects is an open-source repository offering a collection of teaching materials, code, and data specifically designed for data analysis and machine learning projects. Each project typically corresponds to a blog post by Randal S. Olson, providing practical examples and insights. Users can access documentation, often in IPython Notebook format, to understand the analysis, data usage terms, and more. The instructional materials are licensed under Creative Commons Attribution, allowing for sharing and adaptation, even commercially, with proper attribution. The software components are provided under the MIT license, granting broad permissions for use, modification, and distribution. This resource is ideal for those looking to learn from or contribute to real-world data science applications.

RAISE Liguria is an innovation ecosystem focused on Robotics and AI for Socio-economic Empowerment, funded by the Italian Ministry of University and Research. It brings together 26 academic, research, institutional, and business partners, coordinated by the University of Genoa, CNR, and IIT. The project aims to develop and commercialize technology solutions based on robotics and AI to meet the productive and social needs of the Liguria region, particularly in healthcare, environmental sustainability, smart ports, and accessible smart cities. RAISE facilitates technology transfer from research to market, providing resources and support for sustainable innovation, and seeks to create an attractive ecosystem for companies, investors, and researchers.

Claw4S 2026 is a unique academic conference designed to promote reproducible and executable scientific methods. Unlike traditional conferences that accept static papers, Claw4S requires submissions in the form of executable SKILL markdown files. These skills are runnable workflows that can be executed by AI agents, ensuring transparency and reusability. The submission process involves auto-execution by Claw, structured review by AI agents based on criteria like executability and rigor, and a final human meta-review by expert chairs. The conference offers a substantial prize pool for winners across various categories, encouraging innovation in agent-native science. It emphasizes the importance of methods that run, are executable, and can be forked and improved by others.

DigiChem offers an AI-native synthesis platform for on-demand custom chemical synthesis, enabling innovators to prototype and manufacture custom molecules online. The platform handles chemistry from lab scale to production, focusing on speed and efficiency. Users can design and order molecules online, with transparent pricing and no hidden costs. DigiChem provides full access to process data and ensures users own their material and application IP, without exclusivity obligations. Quotes are typically provided within 24 hours, and rapid synthesis timelines are achieved through AI-optimized process design and robotics automation. This approach significantly compresses traditional R&D timelines, allowing for faster material delivery and accelerating innovation across various specialty chemical applications like battery materials, OLEDs, and biomaterials.

Copernilabs offers the ThuliumX Defense AI Platform, an AI-driven network intelligence solution designed for defense command and control. This pure software platform integrates multi-source data from client hardware like cameras, drones, and IoT sensors, using standard protocols. ThuliumX employs AI-driven network intelligence, including multi-modal scene understanding and neuro-symbolic reasoning, to provide real-time battlespace awareness across air, land, sea, space, and cyber domains. It delivers integrated C2 outputs with autonomous action triggering, connecting to existing tactical displays and client systems. The platform is designed for air-gapped deployment, zero vendor lock-in, and runs on existing client infrastructure, ensuring data sovereignty and security.

Tylo AI is an innovation assistant designed to support users in evidence-based inquiry, specifically focusing on deeptech innovation. It allows users to track inquiries and generate responses based on aggregated evidence. The tool ensures users can access data-driven insights efficiently by providing top evidence and references. Tylo AI facilitates research capabilities for individuals and teams, helping to transform complex technology research into practical business insights.

Liffery is an online research and wish-listing tool designed to provide a quiet space for users to capture, consider, and decide on products and information. It aims to eliminate the noise and distractions of typical online shopping platforms, offering a push-ad-free experience. Users can save items of interest, set reminders, and utilize price watchers to make informed purchasing decisions. The platform also supports collaborative channels for groups to share and decide on items, and a 'Decision Maker' feature for quick group consensus. Liffery is free to use and focuses on giving users control over their online research and shopping journey.

DeepLearningInMedicalImagingAndMedicalImageAnalysis is an open-source GitHub repository dedicated to curating resources for deep learning in medical imaging and medical image analysis. It serves as a valuable hub for researchers, developers, and practitioners interested in the intersection of AI and healthcare. The repository compiles reviews, surveys, and various materials that shed light on the current state and future potential of deep learning techniques in medical contexts. Users can explore a wide range of topics, from diagnostic support to image segmentation and reconstruction, gaining insights into how AI is transforming medical diagnostics and treatment planning. This resource is ideal for those looking to understand the theoretical foundations and practical applications of deep learning in this specialized field.

Deep RL Course Certification is a specialized tool developed by Hugging Face to validate the completion of their Deep Reinforcement Learning Course. Users provide their Hugging Face username, first name, and last name to initiate the verification process. The application then assesses the user's model results to confirm successful completion of the course requirements. This certification serves as a valuable credential for individuals looking to demonstrate their proficiency in deep reinforcement learning, making it particularly useful for students and AI practitioners who have undertaken the Hugging Face course.

Deepseek-ai-DeepSeek-R1-0528 Demo offers a direct way to interact with and evaluate the DeepSeek-R1-0528 AI model. This Hugging Face Space allows users to input text prompts and observe the model's natural language generation capabilities. It serves as a practical showcase for the R1 version of the DeepSeek AI model, enabling developers, researchers, and AI enthusiasts to quickly understand its performance and response quality. To access and utilize this demo, users are required to sign in with a Hugging Face account, ensuring a controlled environment for interaction with the model.

Magisterium AI serves as the world's leading answer engine for the Catholic Church, offering cited responses derived from a vast library of over 6,700 documents. This includes the Extraordinary and Ordinary Magisterium, seminal works from Doctors and Fathers of the Church, the Bible, and various biblical commentaries. Users can access a free account to receive 90 queries per week, along with additional features and personalized experiences. The platform is designed to provide accurate and referenced information for those seeking insights into Catholic teachings and history.